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   "source": [
    "# COVID-19 Deaths & Cases Per Capita\n",
    "> Compare deaths and total cases adjusting for population size. \n",
    "\n",
    "- comments: true\n",
    "- author: Joao B. Duarte & Hamel Husain\n",
    "- categories: [growth, compare, interactive]\n",
    "- hide: false\n",
    "- image: images/covid-permillion-trajectories.png\n",
    "- permalink: /covid-compare-permillion/"
   ]
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   "source": [
    "#hide\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import altair as alt\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "chart_width = 550\n",
    "chart_height= 400"
   ]
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    "tags": []
   },
   "source": [
    "## Deaths Per Million Of Inhabitants"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "Since reaching at least 1 death per million\n",
    "\n",
    "> Tip: Click (Shift+ for multiple) on countries in the legend to filter the visualization."
   ]
  },
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   "source": [
    "#hide \n",
    "data = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv\", \n",
    "                   error_bad_lines=False)\n",
    "data = data.drop(columns=[\"Lat\", \"Long\"])\n",
    "data = data.melt(id_vars= [\"Province/State\", \"Country/Region\"])\n",
    "data = pd.DataFrame(data.groupby(['Country/Region', \"variable\"]).sum())\n",
    "data.reset_index(inplace=True)  \n",
    "data = data.rename(columns={\"Country/Region\": \"location\", \"variable\": \"date\", \"value\": \"total_cases\"})\n",
    "data['date'] =pd.to_datetime(data.date)\n",
    "data = data.sort_values(by = \"date\")\n",
    "data.loc[data.location == \"US\",\"location\"] = \"United States\"\n",
    "data.loc[data.location == \"Korea, South\",\"location\"] = \"South Korea\"\n",
    "\n",
    "data_pwt = pd.read_stata(\"https://www.rug.nl/ggdc/docs/pwt91.dta\")\n",
    "\n",
    "filter1 = data_pwt[\"year\"] == 2017\n",
    "data_pop = data_pwt[filter1]\n",
    "data_pop = data_pop[[\"country\",\"pop\"]]\n",
    "data_pop.loc[data_pop.country == \"Republic of Korea\",\"country\"] = \"South Korea\"\n",
    "data_pop.loc[data_pop.country == \"Iran (Islamic Republic of)\",\"country\"] = \"Iran\"\n",
    "\n",
    "# per habitant\n",
    "data_pc = data.copy()\n",
    "countries = [\"China\", \"Italy\", \"Spain\", \"France\", \"United Kingdom\", \"Germany\", \n",
    "             \"Portugal\", \"United States\", \"Singapore\", \"South Korea\", \"Japan\", \n",
    "             \"Brazil\", \"Iran\", 'Netherlands', 'Belgium', 'Sweden', \n",
    "             'Switzerland', 'Norway', 'Denmark', 'Austria', 'Slovenia', 'Greece',\n",
    "             'Cyprus']\n",
    "data_countries = []\n",
    "data_countries_pc = []\n",
    "\n",
    "MIN_DEATHS = 10\n",
    "filter_min_dead = data_pc.total_cases < MIN_DEATHS\n",
    "data_pc = data_pc.drop(data_pc[filter_min_dead].index)\n",
    "\n",
    "# compute per habitant\n",
    "for i in countries:\n",
    "    data_pc.loc[data_pc.location == i,\"total_cases\"] = data_pc.loc[data_pc.location == i,\"total_cases\"]/float(data_pop.loc[data_pop.country == i, \"pop\"])\n",
    "\n",
    "    # get each country time series\n",
    "filter1 = data_pc[\"total_cases\"] > 1\n",
    "for i in countries:\n",
    "    filter_country = data_pc[\"location\"]== i\n",
    "    data_countries_pc.append(data_pc[filter_country & filter1])"
   ]
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\"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5348, \"location\": \"Greece\", \"date\": \"2020-03-24T00:00:00\", \"total_cases\": 1.7921511690011938, \"n_days\": 3, \"log_cases\": 0.583416668696387, \"trend_2days\": 1.0397207708399179, \"trend_4days\": 0.5198603854199589, \"trend_12days\": 0.17328679513998632, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5349, \"location\": \"Greece\", \"date\": \"2020-03-25T00:00:00\", \"total_cases\": 1.9713662859013132, \"n_days\": 4, \"log_cases\": 0.6787268485007119, \"trend_2days\": 1.3862943611198906, \"trend_4days\": 0.6931471805599453, \"trend_12days\": 0.23104906018664842, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5350, \"location\": \"Greece\", \"date\": \"2020-03-26T00:00:00\", \"total_cases\": 2.329796519701552, \"n_days\": 5, \"log_cases\": 0.845780933163878, \"trend_2days\": 1.7328679513998633, \"trend_4days\": 0.8664339756999316, \"trend_12days\": 0.28881132523331055, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5351, \"location\": \"Greece\", \"date\": \"2020-03-27T00:00:00\", \"total_cases\": 2.5090116366016715, \"n_days\": 6, \"log_cases\": 0.9198889053175999, \"trend_2days\": 2.0794415416798357, \"trend_4days\": 1.0397207708399179, \"trend_12days\": 0.34657359027997264, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5352, \"location\": \"Greece\", \"date\": \"2020-03-28T00:00:00\", \"total_cases\": 2.86744187040191, \"n_days\": 7, \"log_cases\": 1.0534202979421226, \"trend_2days\": 2.4260151319598084, \"trend_4days\": 1.2130075659799042, \"trend_12days\": 0.40433585532663474, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5353, \"location\": \"Greece\", \"date\": \"2020-03-29T00:00:00\", \"total_cases\": 3.4050872211022685, \"n_days\": 8, \"log_cases\": 1.225270554868782, \"trend_2days\": 2.772588722239781, \"trend_4days\": 1.3862943611198906, \"trend_12days\": 0.46209812037329684, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5355, \"location\": \"Greece\", \"date\": \"2020-03-30T00:00:00\", \"total_cases\": 3.8531250133525665, \"n_days\": 9, \"log_cases\": 1.3488845108359584, \"trend_2days\": 3.119162312519754, \"trend_4days\": 1.559581156259877, \"trend_12days\": 0.5198603854199589, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5356, \"location\": \"Greece\", \"date\": \"2020-03-31T00:00:00\", \"total_cases\": 4.3907703640529245, \"n_days\": 10, \"log_cases\": 1.4795046932530225, \"trend_2days\": 3.4657359027997265, \"trend_4days\": 1.7328679513998633, \"trend_12days\": 0.5776226504666211, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5363, \"location\": \"Greece\", \"date\": \"2020-04-01T00:00:00\", \"total_cases\": 4.480377922502985, \"n_days\": 11, \"log_cases\": 1.4997074005705422, \"trend_2days\": 3.8123094930796992, \"trend_4days\": 1.9061547465398496, \"trend_12days\": 0.6353849155132831, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5364, \"location\": \"Greece\", \"date\": \"2020-04-02T00:00:00\", \"total_cases\": 4.7492005978531635, \"n_days\": 12, \"log_cases\": 1.5579763086945178, \"trend_2days\": 4.1588830833596715, \"trend_4days\": 2.0794415416798357, \"trend_12days\": 0.6931471805599453, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5365, \"location\": \"Greece\", \"date\": \"2020-04-03T00:00:00\", \"total_cases\": 5.6452761823537605, \"n_days\": 13, \"log_cases\": 1.7308191215339286, \"trend_2days\": 4.505456673639644, \"trend_4days\": 2.252728336819822, \"trend_12days\": 0.7509094456066073, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5366, \"location\": \"Greece\", \"date\": \"2020-04-04T00:00:00\", \"total_cases\": 6.093313974604059, \"n_days\": 14, \"log_cases\": 1.8071921003185027, \"trend_2days\": 4.852030263919617, \"trend_4days\": 2.4260151319598084, \"trend_12days\": 0.8086717106532695, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5367, \"location\": \"Greece\", \"date\": \"2020-04-05T00:00:00\", \"total_cases\": 6.5413517668543575, \"n_days\": 15, \"log_cases\": 1.8781438362907872, \"trend_2days\": 5.19860385419959, \"trend_4days\": 2.599301927099795, \"trend_12days\": 0.8664339756999315, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5368, \"location\": \"Greece\", \"date\": \"2020-04-06T00:00:00\", \"total_cases\": 7.0789971175547155, \"n_days\": 16, \"log_cases\": 1.9571322476094175, \"trend_2days\": 5.545177444479562, \"trend_4days\": 2.772588722239781, \"trend_12days\": 0.9241962407465937, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5369, \"location\": \"Greece\", \"date\": \"2020-04-07T00:00:00\", \"total_cases\": 7.258212234454835, \"n_days\": 17, \"log_cases\": 1.9821335498148347, \"trend_2days\": 5.891751034759535, \"trend_4days\": 2.9458755173797675, \"trend_12days\": 0.9819585057932558, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5370, \"location\": \"Greece\", \"date\": \"2020-04-08T00:00:00\", \"total_cases\": 7.4374273513549545, \"n_days\": 18, \"log_cases\": 2.006525002938994, \"trend_2days\": 6.238324625039508, \"trend_4days\": 3.119162312519754, \"trend_12days\": 1.0397207708399179, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 5371, \"location\": \"Greece\", \"date\": \"2020-04-09T00:00:00\", \"total_cases\": 7.7958575851551934, \"n_days\": 19, \"log_cases\": 2.0535925137969797, \"trend_2days\": 6.58489821531948, \"trend_4days\": 3.29244910765974, \"trend_12days\": 1.09748303588658, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 3547, \"location\": \"Cyprus\", \"date\": \"2020-04-02T00:00:00\", \"total_cases\": 11.693813524654866, \"n_days\": 0, \"log_cases\": 2.45905994338509, \"trend_2days\": 0.0, \"trend_4days\": 0.0, \"trend_12days\": 0.0, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 3548, \"location\": \"Cyprus\", \"date\": \"2020-04-03T00:00:00\", \"total_cases\": 12.863194877120351, \"n_days\": 1, \"log_cases\": 2.5543701231894147, \"trend_2days\": 0.34657359027997264, \"trend_4days\": 0.17328679513998632, \"trend_12days\": 0.057762265046662105, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 3549, \"location\": \"Cyprus\", \"date\": \"2020-04-04T00:00:00\", \"total_cases\": 12.863194877120351, \"n_days\": 2, \"log_cases\": 2.5543701231894147, \"trend_2days\": 0.6931471805599453, \"trend_4days\": 0.34657359027997264, \"trend_12days\": 0.11552453009332421, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 3554, \"location\": \"Cyprus\", \"date\": \"2020-04-09T00:00:00\", \"total_cases\": 11.693813524654866, \"n_days\": 3, \"log_cases\": 2.45905994338509, \"trend_2days\": 1.0397207708399179, \"trend_4days\": 0.5198603854199589, \"trend_12days\": 0.17328679513998632, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}], \"data-cf5cfaa2f9f67721e9c0baa1e72af7ac\": [{\"label\": \"Doubles every 2 days\", \"x_coord\": 6, \"y_coord\": 4.0}, {\"label\": \"Doubles every 4 days\", \"x_coord\": 16, \"y_coord\": 3.5}, {\"label\": \"Doubles every 12 days\", \"x_coord\": 25, \"y_coord\": 1.8}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "# Stack data to get it to Altair dataframe format\n",
    "data_countries_pc2 = data_countries_pc.copy()\n",
    "for i in range(0,len(countries)):\n",
    "    data_countries_pc2[i] = data_countries_pc2[i].reset_index()\n",
    "    data_countries_pc2[i]['n_days'] = data_countries_pc2[i].index\n",
    "    data_countries_pc2[i]['log_cases'] = np.log(data_countries_pc2[i][\"total_cases\"])\n",
    "data_plot = data_countries_pc2[0]\n",
    "for i in range(1, len(countries)):    \n",
    "    data_plot = pd.concat([data_plot, data_countries_pc2[i]], axis=0)\n",
    "data_plot[\"trend_2days\"] = np.log(2)/2*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_4days\"] = np.log(2)/4*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_12days\"] = np.log(2)/12*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_2days_label\"] = \"Doubles every 2 days\"\n",
    "data_plot[\"trend_4days_label\"] = \"Doubles evey 4 days\"\n",
    "data_plot[\"trend_12days_label\"] = \"Doubles every 12 days\"\n",
    "\n",
    "\n",
    "# Plot it using Altair\n",
    "source = data_plot\n",
    "\n",
    "scales = alt.selection_interval(bind='scales')\n",
    "selection = alt.selection_multi(fields=['location'], bind='legend')\n",
    "\n",
    "base = alt.Chart(source, title = \"COVID-19 Deaths Per Million of Inhabitants\").encode(\n",
    "    x = alt.X('n_days:Q', title = \"Days passed since reaching 1 death per million\"),\n",
    "    y = alt.Y(\"log_cases:Q\",title = \"Log of deaths per million\"),\n",
    "    color = alt.Color('location:N', legend=alt.Legend(title=\"Country\", labelFontSize=15, titleFontSize=17),\n",
    "                     scale=alt.Scale(scheme='tableau20')),\n",
    "    opacity = alt.condition(selection, alt.value(1), alt.value(0.1))\n",
    ")\n",
    "\n",
    "lines = base.mark_line().add_selection(\n",
    "    scales\n",
    ").add_selection(\n",
    "    selection\n",
    ").properties(\n",
    "    width=chart_width,\n",
    "    height=chart_height\n",
    ")\n",
    "\n",
    "trend_2d = alt.Chart(source).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_2days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ").mark_line(color=\"grey\", strokeDash=[3,3])\n",
    "\n",
    "\n",
    "labels = pd.DataFrame([{'label': 'Doubles every 2 days', 'x_coord': 6, 'y_coord': 4},\n",
    "                       {'label': 'Doubles every 4 days', 'x_coord': 16, 'y_coord': 3.5},\n",
    "                       {'label': 'Doubles every 12 days', 'x_coord': 25, 'y_coord': 1.8},\n",
    "                      ])\n",
    "trend_label = (alt.Chart(labels)\n",
    "                    .mark_text(align='left', dx=-55, dy=-15, fontSize=12, color=\"grey\")\n",
    "                    .encode(x='x_coord:Q',\n",
    "                            y='y_coord:Q',\n",
    "                            text='label:N')\n",
    "                   )\n",
    "\n",
    "trend_4d = alt.Chart(source).mark_line(color=\"grey\", strokeDash=[3,3]).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_4days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ")\n",
    "\n",
    "trend_12d = alt.Chart(source).mark_line(color=\"grey\", strokeDash=[3,3]).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_12days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ")\n",
    "\n",
    "\n",
    "plot1= (\n",
    "(trend_2d + trend_4d + trend_12d + trend_label + lines)\n",
    ".configure_title(fontSize=20)\n",
    ".configure_axis(labelFontSize=15,titleFontSize=18)\n",
    ")\n",
    "plot1.save((\"../images/covid-permillion-trajectories.png\"))\n",
    "plot1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.012381,
     "end_time": "2020-04-10T00:18:20.157731",
     "exception": false,
     "start_time": "2020-04-10T00:18:20.145350",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Last Available Total Deaths By Country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-10T00:18:20.191555Z",
     "iopub.status.busy": "2020-04-10T00:18:20.191013Z",
     "iopub.status.idle": "2020-04-10T00:18:20.218229Z",
     "shell.execute_reply": "2020-04-10T00:18:20.218869Z"
    },
    "papermill": {
     "duration": 0.048999,
     "end_time": "2020-04-10T00:18:20.219030",
     "exception": false,
     "start_time": "2020-04-10T00:18:20.170031",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Deaths per Million</th>\n",
       "      <th>Log of Deaths per Million</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>33.770431</td>\n",
       "      <td>3.519586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>220.747740</td>\n",
       "      <td>5.397021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>4.539193</td>\n",
       "      <td>1.512749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>2.368896</td>\n",
       "      <td>0.862424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cyprus</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>11.693814</td>\n",
       "      <td>2.459060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Denmark</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>41.335640</td>\n",
       "      <td>3.721725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>181.892966</td>\n",
       "      <td>5.203418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>31.748457</td>\n",
       "      <td>3.457844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Greece</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>7.795858</td>\n",
       "      <td>2.053593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>50.638970</td>\n",
       "      <td>3.924721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>307.935147</td>\n",
       "      <td>5.729889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Netherlands</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>141.054749</td>\n",
       "      <td>4.949148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Norway</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>20.356682</td>\n",
       "      <td>3.013409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>39.595311</td>\n",
       "      <td>3.678711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slovenia</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>20.673315</td>\n",
       "      <td>3.028844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>4.001396</td>\n",
       "      <td>1.386643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>333.237546</td>\n",
       "      <td>5.808856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sweden</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>80.014523</td>\n",
       "      <td>4.382208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switzerland</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>111.845149</td>\n",
       "      <td>4.717115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>120.773773</td>\n",
       "      <td>4.793919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>50.786004</td>\n",
       "      <td>3.927621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date  Deaths per Million  Log of Deaths per Million\n",
       "Country                                                                 \n",
       "Austria        2020-04-09           33.770431                   3.519586\n",
       "Belgium        2020-04-09          220.747740                   5.397021\n",
       "Brazil         2020-04-09            4.539193                   1.512749\n",
       "China          2020-04-09            2.368896                   0.862424\n",
       "Cyprus         2020-04-09           11.693814                   2.459060\n",
       "Denmark        2020-04-09           41.335640                   3.721725\n",
       "France         2020-04-09          181.892966                   5.203418\n",
       "Germany        2020-04-09           31.748457                   3.457844\n",
       "Greece         2020-04-09            7.795858                   2.053593\n",
       "Iran           2020-04-09           50.638970                   3.924721\n",
       "Italy          2020-04-09          307.935147                   5.729889\n",
       "Netherlands    2020-04-09          141.054749                   4.949148\n",
       "Norway         2020-04-09           20.356682                   3.013409\n",
       "Portugal       2020-04-09           39.595311                   3.678711\n",
       "Slovenia       2020-04-09           20.673315                   3.028844\n",
       "South Korea    2020-04-09            4.001396                   1.386643\n",
       "Spain          2020-04-09          333.237546                   5.808856\n",
       "Sweden         2020-04-09           80.014523                   4.382208\n",
       "Switzerland    2020-04-09          111.845149                   4.717115\n",
       "United Kingdom 2020-04-09          120.773773                   4.793919\n",
       "United States  2020-04-09           50.786004                   3.927621"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "label = 'Deaths'\n",
    "temp = pd.concat([x.copy() for x in data_countries_pc]).loc[lambda x: x.date >= '3/1/2020']\n",
    "\n",
    "metric_name = f'{label} per Million'\n",
    "temp.columns = ['Country', 'date', metric_name]\n",
    "# temp.loc[:, 'month'] = temp.date.dt.strftime('%Y-%m')\n",
    "temp.loc[:, f'Log of {label} per Million'] = temp[f'{label} per Million'].apply(lambda x: np.log(x))\n",
    "\n",
    "temp.groupby('Country').last()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-10T00:18:20.257132Z",
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     "shell.execute_reply": "2020-04-10T00:18:27.055972Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#hide\n",
    "# Get data and clean it\n",
    "\n",
    "data = pd.read_csv(\"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv\", error_bad_lines=False)\n",
    "data = data.drop(columns=[\"Lat\", \"Long\"])\n",
    "data = data.melt(id_vars= [\"Province/State\", \"Country/Region\"])\n",
    "data = pd.DataFrame(data.groupby(['Country/Region', \"variable\"]).sum())\n",
    "data.reset_index(inplace=True)  \n",
    "data = data.rename(columns={\"Country/Region\": \"location\", \"variable\": \"date\", \"value\": \"total_cases\"})\n",
    "data['date'] =pd.to_datetime(data.date)\n",
    "data = data.sort_values(by = \"date\")\n",
    "data.loc[data.location == \"US\",\"location\"] = \"United States\"\n",
    "data.loc[data.location == \"Korea, South\",\"location\"] = \"South Korea\"\n",
    "\n",
    "# Population data (last year is 2017 which is what we use)\n",
    "data_pwt = pd.read_stata(\"https://www.rug.nl/ggdc/docs/pwt91.dta\")\n",
    "\n",
    "filter1 = data_pwt[\"year\"] == 2017\n",
    "data_pop = data_pwt[filter1]\n",
    "data_pop = data_pop[[\"country\",\"pop\"]]\n",
    "data_pop.loc[data_pop.country == \"Republic of Korea\",\"country\"] = \"South Korea\"\n",
    "data_pop.loc[data_pop.country == \"Iran (Islamic Republic of)\",\"country\"] = \"Iran\"\n",
    "\n",
    "# per habitant\n",
    "data_pc = data.copy()\n",
    "\n",
    "# I can add more countries if needed\n",
    "countries = [\"China\", \"Italy\", \"Spain\", \"France\", \"United Kingdom\", \"Germany\", \n",
    "             \"Portugal\", \"United States\", \"Singapore\",\"South Korea\", \"Japan\", \n",
    "             \"Brazil\",\"Iran\"]\n",
    "\n",
    "data_countries = []\n",
    "data_countries_pc = []\n",
    "\n",
    "# compute per habitant\n",
    "for i in countries:\n",
    "    data_pc.loc[data_pc.location == i,\"total_cases\"] = data_pc.loc[data_pc.location == i,\"total_cases\"]/float(data_pop.loc[data_pop.country == i, \"pop\"])\n",
    "    \n",
    "# get each country time series\n",
    "filter1 = data_pc[\"total_cases\"] > 1\n",
    "\n",
    "for i in countries:\n",
    "    filter_country = data_pc[\"location\"]== i\n",
    "    data_countries_pc.append(data_pc[filter_country & filter1])"
   ]
  },
  {
   "cell_type": "markdown",
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    "papermill": {
     "duration": 0.011921,
     "end_time": "2020-04-10T00:18:27.081444",
     "exception": false,
     "start_time": "2020-04-10T00:18:27.069523",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Cases Per Million of Habitants\n",
    "\n",
    "Since reaching at least 1 case per million\n",
    "\n",
    "> Note: The following chart, \"Cases Per Million of Habitants\" is biased depending on how widely a country administers tests. Please read with caution.\n",
    "\n",
    "> Tip: Click (Shift+ for multiple) on countries in the legend to filter the visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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     "start_time": "2020-04-10T00:18:27.093769",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
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4.205525583551638, \"trend_2days\": 7.278045395879426, \"trend_4days\": 3.639022697939713, \"trend_12days\": 1.2130075659799042, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 1894, \"location\": \"Brazil\", \"date\": \"2020-04-08T00:00:00\", \"total_cases\": 77.26184986392087, \"n_days\": 22, \"log_cases\": 4.347200300310556, \"trend_2days\": 7.6246189861593985, \"trend_4days\": 3.8123094930796992, \"trend_12days\": 1.2707698310265663, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 1895, \"location\": \"Brazil\", \"date\": \"2020-04-09T00:00:00\", \"total_cases\": 86.44535483846978, \"n_days\": 23, \"log_cases\": 4.459512478337137, \"trend_2days\": 7.971192576439371, \"trend_4days\": 3.9855962882196856, \"trend_12days\": 1.3285320960732283, 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3.119162312519754, \"trend_12days\": 1.0397207708399179, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6365, \"location\": \"Iran\", \"date\": \"2020-03-15T00:00:00\", \"total_cases\": 171.7289447084362, \"n_days\": 19, \"log_cases\": 5.145917330914502, \"trend_2days\": 6.58489821531948, \"trend_4days\": 3.29244910765974, \"trend_12days\": 1.09748303588658, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6366, \"location\": \"Iran\", \"date\": \"2020-03-16T00:00:00\", \"total_cases\": 184.7028705785742, \"n_days\": 20, \"log_cases\": 5.218748428928353, \"trend_2days\": 6.931471805599453, \"trend_4days\": 3.4657359027997265, \"trend_12days\": 1.1552453009332422, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6367, \"location\": \"Iran\", \"date\": \"2020-03-17T00:00:00\", \"total_cases\": 199.21691110566113, \"n_days\": 21, \"log_cases\": 5.294394236655853, \"trend_2days\": 7.278045395879426, \"trend_4days\": 3.639022697939713, \"trend_12days\": 1.2130075659799042, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6368, \"location\": \"Iran\", \"date\": \"2020-03-18T00:00:00\", \"total_cases\": 213.90344447432636, \"n_days\": 22, \"log_cases\": 5.365524719157986, \"trend_2days\": 7.6246189861593985, \"trend_4days\": 3.8123094930796992, \"trend_12days\": 1.2707698310265663, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6369, \"location\": \"Iran\", \"date\": \"2020-03-19T00:00:00\", \"total_cases\": 226.7911239236752, \"n_days\": 23, \"log_cases\": 5.42402943494876, \"trend_2days\": 7.971192576439371, \"trend_4days\": 3.9855962882196856, \"trend_12days\": 1.3285320960732283, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6371, \"location\": \"Iran\", \"date\": \"2020-03-20T00:00:00\", \"total_cases\": 242.03209856884206, \"n_days\": 24, \"log_cases\": 5.489070356075174, \"trend_2days\": 8.317766166719343, \"trend_4days\": 4.1588830833596715, \"trend_12days\": 1.3862943611198906, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6372, \"location\": \"Iran\", \"date\": \"2020-03-21T00:00:00\", \"total_cases\": 253.9341046377436, \"n_days\": 25, \"log_cases\": 5.537074802800543, \"trend_2days\": 8.664339756999317, \"trend_4days\": 4.332169878499658, \"trend_12days\": 1.4440566261665526, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6373, \"location\": \"Iran\", \"date\": \"2020-03-22T00:00:00\", \"total_cases\": 266.6000075764918, \"n_days\": 26, \"log_cases\": 5.585749436163557, \"trend_2days\": 9.010913347279288, \"trend_4days\": 4.505456673639644, \"trend_12days\": 1.5018188912132147, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6374, \"location\": \"Iran\", \"date\": \"2020-03-23T00:00:00\", \"total_cases\": 283.9848218241316, \"n_days\": 27, \"log_cases\": 5.648920792451783, \"trend_2days\": 9.357486937559262, \"trend_4days\": 4.678743468779631, \"trend_12days\": 1.559581156259877, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6375, \"location\": \"Iran\", \"date\": \"2020-03-24T00:00:00\", \"total_cases\": 305.69427802848406, \"n_days\": 28, \"log_cases\": 5.7225855111177, \"trend_2days\": 9.704060527839234, \"trend_4days\": 4.852030263919617, \"trend_12days\": 1.617343421306539, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6376, \"location\": \"Iran\", \"date\": \"2020-03-25T00:00:00\", \"total_cases\": 332.87422149431916, \"n_days\": 29, \"log_cases\": 5.8077647053985615, \"trend_2days\": 10.050634118119207, \"trend_4days\": 5.025317059059604, \"trend_12days\": 1.675105686353201, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6377, \"location\": \"Iran\", \"date\": \"2020-03-26T00:00:00\", \"total_cases\": 362.3088928179276, \"n_days\": 30, \"log_cases\": 5.892497143053641, \"trend_2days\": 10.39720770839918, \"trend_4days\": 5.19860385419959, \"trend_12days\": 1.732867951399863, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6378, \"location\": \"Iran\", \"date\": \"2020-03-27T00:00:00\", \"total_cases\": 398.3598967077887, \"n_days\": 31, \"log_cases\": 5.987355859769773, \"trend_2days\": 10.743781298679153, \"trend_4days\": 5.371890649339576, \"trend_12days\": 1.7906302164465253, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6379, \"location\": \"Iran\", \"date\": \"2020-03-28T00:00:00\", \"total_cases\": 436.2590381859886, \"n_days\": 32, \"log_cases\": 6.0782361912068685, \"trend_2days\": 11.090354888959125, \"trend_4days\": 5.545177444479562, \"trend_12days\": 1.8483924814931874, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6380, \"location\": \"Iran\", \"date\": \"2020-03-29T00:00:00\", \"total_cases\": 472.0020191444599, \"n_days\": 33, \"log_cases\": 6.156983263424838, \"trend_2days\": 11.436928479239096, \"trend_4days\": 5.718464239619548, \"trend_12days\": 1.9061547465398494, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6382, \"location\": \"Iran\", \"date\": \"2020-03-30T00:00:00\", \"total_cases\": 511.25646152077485, \"n_days\": 34, \"log_cases\": 6.236871345948586, \"trend_2days\": 11.78350206951907, \"trend_4days\": 5.891751034759535, \"trend_12days\": 1.9639170115865117, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6383, \"location\": \"Iran\", \"date\": \"2020-03-31T00:00:00\", \"total_cases\": 549.5745141856648, \"n_days\": 35, \"log_cases\": 6.309144368263922, \"trend_2days\": 12.130075659799042, \"trend_4days\": 6.065037829899521, \"trend_12days\": 2.0216792766331735, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6390, \"location\": \"Iran\", \"date\": \"2020-04-01T00:00:00\", \"total_cases\": 586.3894149453727, \"n_days\": 36, \"log_cases\": 6.373984099497811, \"trend_2days\": 12.476649250079015, \"trend_4days\": 6.238324625039508, \"trend_12days\": 2.0794415416798357, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6391, \"location\": \"Iran\", \"date\": \"2020-04-02T00:00:00\", \"total_cases\": 621.8120520551986, \"n_days\": 37, \"log_cases\": 6.43263787996367, \"trend_2days\": 12.823222840358987, \"trend_4days\": 6.411611420179494, \"trend_12days\": 2.137203806726498, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6392, \"location\": \"Iran\", \"date\": \"2020-04-03T00:00:00\", \"total_cases\": 655.2633424041298, \"n_days\": 38, \"log_cases\": 6.485037204306973, \"trend_2days\": 13.16979643063896, \"trend_4days\": 6.58489821531948, \"trend_12days\": 2.19496607177316, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6393, \"location\": \"Iran\", \"date\": \"2020-04-04T00:00:00\", \"total_cases\": 686.8048905784444, \"n_days\": 39, \"log_cases\": 6.532050249816902, \"trend_2days\": 13.516370020918933, \"trend_4days\": 6.758185010459466, \"trend_12days\": 2.252728336819822, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6394, \"location\": \"Iran\", \"date\": \"2020-04-05T00:00:00\", \"total_cases\": 717.3977281240785, \"n_days\": 40, \"log_cases\": 6.575630398274209, \"trend_2days\": 13.862943611198906, \"trend_4days\": 6.931471805599453, \"trend_12days\": 2.3104906018664844, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6395, \"location\": \"Iran\", \"date\": \"2020-04-06T00:00:00\", \"total_cases\": 745.4154939632939, \"n_days\": 41, \"log_cases\": 6.613941772934971, \"trend_2days\": 14.209517201478878, \"trend_4days\": 7.104758600739439, \"trend_12days\": 2.368252866913146, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6396, \"location\": \"Iran\", \"date\": \"2020-04-07T00:00:00\", \"total_cases\": 771.1538901102248, \"n_days\": 42, \"log_cases\": 6.647887951714022, \"trend_2days\": 14.556090791758852, \"trend_4days\": 7.278045395879426, \"trend_12days\": 2.4260151319598084, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6397, \"location\": \"Iran\", \"date\": \"2020-04-08T00:00:00\", \"total_cases\": 795.7587618696413, \"n_days\": 43, \"log_cases\": 6.679296076933681, \"trend_2days\": 14.902664382038823, \"trend_4days\": 7.451332191019412, \"trend_12days\": 2.4837773970064707, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}, {\"index\": 6398, \"location\": \"Iran\", \"date\": \"2020-04-09T00:00:00\", \"total_cases\": 815.891140665278, \"n_days\": 44, \"log_cases\": 6.704280940017275, \"trend_2days\": 15.249237972318797, \"trend_4days\": 7.6246189861593985, \"trend_12days\": 2.5415396620531325, \"trend_2days_label\": \"Doubles every 2 days\", \"trend_4days_label\": \"Doubles evey 4 days\", \"trend_12days_label\": \"Doubles every 12 days\"}], \"data-2cf9e9c9dd4f97c8eda44cd9355d02e6\": [{\"label\": \"Doubles every 2 days\", \"x_coord\": 10, \"y_coord\": 6}, {\"label\": \"Doubles every 4 days\", \"x_coord\": 28, \"y_coord\": 6}, {\"label\": \"Doubles every 12 days\", \"x_coord\": 45, \"y_coord\": 3}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.LayerChart(...)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "# Stack data to get it to Altair dataframe format\n",
    "data_countries_pc2 = data_countries_pc.copy()\n",
    "for i in range(0,len(countries)):\n",
    "    data_countries_pc2[i] = data_countries_pc2[i].reset_index()\n",
    "    data_countries_pc2[i]['n_days'] = data_countries_pc2[i].index\n",
    "    data_countries_pc2[i]['log_cases'] = np.log(data_countries_pc2[i][\"total_cases\"])\n",
    "data_plot = data_countries_pc2[0]\n",
    "for i in range(1, len(countries)):    \n",
    "    data_plot = pd.concat([data_plot, data_countries_pc2[i]], axis=0)\n",
    "data_plot[\"trend_2days\"] = np.log(2)/2*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_4days\"] = np.log(2)/4*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_12days\"] = np.log(2)/12*data_plot[\"n_days\"]\n",
    "data_plot[\"trend_2days_label\"] = \"Doubles every 2 days\"\n",
    "data_plot[\"trend_4days_label\"] = \"Doubles evey 4 days\"\n",
    "data_plot[\"trend_12days_label\"] = \"Doubles every 12 days\"\n",
    "\n",
    "\n",
    "# Plot it using Altair\n",
    "source = data_plot\n",
    "\n",
    "scales = alt.selection_interval(bind='scales')\n",
    "selection = alt.selection_multi(fields=['location'], bind='legend')\n",
    "\n",
    "base = alt.Chart(source, title = \"COVID-19 Confirmed Cases Per Million of Inhabitants\").encode(\n",
    "    x = alt.X('n_days:Q', title = \"Days passed since reaching 1 case per million\"),\n",
    "    y = alt.Y(\"log_cases:Q\",title = \"Log of confirmed cases per million\"),\n",
    "    color = alt.Color('location:N', legend=alt.Legend(title=\"Country\", labelFontSize=15, titleFontSize=17),\n",
    "                     scale=alt.Scale(scheme='tableau20')),\n",
    "    opacity = alt.condition(selection, alt.value(1), alt.value(0.1))\n",
    ").properties(\n",
    "    width=chart_width,\n",
    "    height=chart_height\n",
    ")\n",
    "\n",
    "lines = base.mark_line().add_selection(\n",
    "    scales\n",
    ").add_selection(\n",
    "    selection\n",
    ")\n",
    "\n",
    "trend_2d = alt.Chart(source).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_2days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ").mark_line( strokeDash=[3,3], color=\"grey\")\n",
    "\n",
    "labels = pd.DataFrame([{'label': 'Doubles every 2 days', 'x_coord': 10, 'y_coord': 6},\n",
    "                       {'label': 'Doubles every 4 days', 'x_coord': 28, 'y_coord': 6},\n",
    "                       {'label': 'Doubles every 12 days', 'x_coord': 45, 'y_coord': 3},\n",
    "                      ])\n",
    "trend_label = (alt.Chart(labels)\n",
    "                    .mark_text(align='left', dx=-55, dy=-15, fontSize=12, color=\"grey\")\n",
    "                    .encode(x='x_coord:Q',\n",
    "                            y='y_coord:Q',\n",
    "                            text='label:N')\n",
    "                   )\n",
    "\n",
    "\n",
    "trend_4d = alt.Chart(source).mark_line(color=\"grey\", strokeDash=[3,3]).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_4days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ")\n",
    "\n",
    "trend_12d = alt.Chart(source).mark_line(color=\"grey\", strokeDash=[3,3]).encode(\n",
    "    x = \"n_days:Q\",\n",
    "    y = alt.Y(\"trend_12days:Q\",  scale=alt.Scale(domain=(0, max(data_plot[\"log_cases\"])))),\n",
    ")\n",
    "\n",
    "(\n",
    "(trend_2d  + trend_4d + trend_12d + trend_label + lines)\n",
    ".configure_title(fontSize=20)\n",
    ".configure_axis(labelFontSize=15,titleFontSize=18)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014616,
     "end_time": "2020-04-10T00:18:27.353816",
     "exception": false,
     "start_time": "2020-04-10T00:18:27.339200",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Appendix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014865,
     "end_time": "2020-04-10T00:18:27.383741",
     "exception": false,
     "start_time": "2020-04-10T00:18:27.368876",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "Last Available Cases Per Million By Country:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-04-10T00:18:27.425434Z",
     "iopub.status.busy": "2020-04-10T00:18:27.423128Z",
     "iopub.status.idle": "2020-04-10T00:18:27.442146Z",
     "shell.execute_reply": "2020-04-10T00:18:27.442556Z"
    },
    "papermill": {
     "duration": 0.042966,
     "end_time": "2020-04-10T00:18:27.442672",
     "exception": false,
     "start_time": "2020-04-10T00:18:27.399706",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Cases per Million</th>\n",
       "      <th>Log of Cases per Million</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>86.445355</td>\n",
       "      <td>4.459512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>58.802393</td>\n",
       "      <td>4.074183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>1766.881617</td>\n",
       "      <td>7.476971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>1439.226842</td>\n",
       "      <td>7.271861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>815.891141</td>\n",
       "      <td>6.704281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>2419.579490</td>\n",
       "      <td>7.791349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>36.608386</td>\n",
       "      <td>3.600277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portugal</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>1351.081079</td>\n",
       "      <td>7.208660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Singapore</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>334.568599</td>\n",
       "      <td>5.812842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Korea</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>204.443856</td>\n",
       "      <td>5.320293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>3305.452399</td>\n",
       "      <td>8.103329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United Kingdom</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>995.322155</td>\n",
       "      <td>6.903066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>United States</th>\n",
       "      <td>2020-04-09</td>\n",
       "      <td>1422.171454</td>\n",
       "      <td>7.259940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     date  Cases per Million  Log of Cases per Million\n",
       "Country                                                               \n",
       "Brazil         2020-04-09          86.445355                  4.459512\n",
       "China          2020-04-09          58.802393                  4.074183\n",
       "France         2020-04-09        1766.881617                  7.476971\n",
       "Germany        2020-04-09        1439.226842                  7.271861\n",
       "Iran           2020-04-09         815.891141                  6.704281\n",
       "Italy          2020-04-09        2419.579490                  7.791349\n",
       "Japan          2020-04-09          36.608386                  3.600277\n",
       "Portugal       2020-04-09        1351.081079                  7.208660\n",
       "Singapore      2020-04-09         334.568599                  5.812842\n",
       "South Korea    2020-04-09         204.443856                  5.320293\n",
       "Spain          2020-04-09        3305.452399                  8.103329\n",
       "United Kingdom 2020-04-09         995.322155                  6.903066\n",
       "United States  2020-04-09        1422.171454                  7.259940"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#hide_input\n",
    "label = 'Cases'\n",
    "temp = pd.concat([x.copy() for x in data_countries_pc]).loc[lambda x: x.date >= '3/1/2020']\n",
    "\n",
    "metric_name = f'{label} per Million'\n",
    "temp.columns = ['Country', 'date', metric_name]\n",
    "# temp.loc[:, 'month'] = temp.date.dt.strftime('%Y-%m')\n",
    "temp.loc[:, f'Log of {label} per Million'] = temp[f'{label} per Million'].apply(lambda x: np.log(x))\n",
    "\n",
    "temp.groupby('Country').last()"
   ]
  },
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    "tags": []
   },
   "source": [
    "This analysis was conducted by [Joao B. Duarte](http://jbduarte.com). Assitance with creating visualizations were provided by [Hamel Husain](https://twitter.com/HamelHusain). Relevant sources are listed below: \n",
    "\n",
    "\n",
    "1. [\"2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE\"](https://systems.jhu.edu/research/public-health/ncov/) [GitHub repository](https://github.com/CSSEGISandData/COVID-19). \n",
    "\n",
    "2. [Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), \"The Next Generation of the Penn World Table\" American Economic Review, 105(10), 3150-3182](https://www.rug.nl/ggdc/productivity/pwt/related-research)"
   ]
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